Numeric.SGD.Sparse.Momentum

A version of Sparse extended with momentum.

Documentation

data SgdArgs

SGD parameters controlling the learning process.

Constructors

SgdArgs

Fields

• batchSize :: Int

  Size of the batch

• regVar :: Double

  Regularization variance

• iterNum :: Double

  Number of iterations

• gain0 :: Double

  Initial gain parameter

• tau :: Double

  After how many iterations over the entire dataset the gain parameter is halved

sgdArgsDefault :: SgdArgs

Default SGD parameter values.

type Para = Vector Double

Vector of parameters.

sgd

Arguments

:: SgdArgs

SGD parameter values

-> (Para -> Int -> IO ())

Notification run every update

-> (Para -> x -> Grad)

Gradient for dataset element

-> DataSet x

DataSet

-> Para

Starting point

-> IO Para

SGD result

A stochastic gradient descent method. A notification function can be used to provide user with information about the progress of the learning.

module Numeric.SGD.Sparse.Grad

module Numeric.SGD.DataSet